The Experts below are selected from a list of 56289 Experts worldwide ranked by ideXlab platform

Stephen A Duncan - One of the best experts on this subject based on the ideXlab platform.

  • optimal operation of an energy management system using model predictive control and gaussian Process time series modeling
    IEEE Journal of Emerging and Selected Topics in Power Electronics, 2018
    Co-Authors: Jaehwa Lee, Pengfei Zhang, Leong Kit Gan, David A Howey, Michael A Osborne, Alessandra Tosi, Stephen A Duncan
    Abstract:

    This paper describes an optimal operation scheme for energy management systems using Gaussian Process Forecasting and model predictive control (MPC) in the context of grid-connected microgrids with local generation, loads, and storage. The main objective of the control is to minimize the cost of energy taken from the grid. The microgrid consists of a photovoltaic (PV) panel and a battery energy storage system, which are connected to a power grid and a local load via a dc bus. At each sampling time, the predictions for PV output power and load demand power are calculated, and an MPC algorithm is executed based on these predictions and a physical battery model to decide the set point of the battery. Simulations of two case studies, namely, a labscale microgrid and a commercial microgrid, are presented. We compare the performance of MPC with various horizon lengths to a rule-based control strategy to demonstrate a cost reduction of more than 2%.

Jaehwa Lee - One of the best experts on this subject based on the ideXlab platform.

  • optimal operation of an energy management system using model predictive control and gaussian Process time series modeling
    IEEE Journal of Emerging and Selected Topics in Power Electronics, 2018
    Co-Authors: Jaehwa Lee, Pengfei Zhang, Leong Kit Gan, David A Howey, Michael A Osborne, Alessandra Tosi, Stephen A Duncan
    Abstract:

    This paper describes an optimal operation scheme for energy management systems using Gaussian Process Forecasting and model predictive control (MPC) in the context of grid-connected microgrids with local generation, loads, and storage. The main objective of the control is to minimize the cost of energy taken from the grid. The microgrid consists of a photovoltaic (PV) panel and a battery energy storage system, which are connected to a power grid and a local load via a dc bus. At each sampling time, the predictions for PV output power and load demand power are calculated, and an MPC algorithm is executed based on these predictions and a physical battery model to decide the set point of the battery. Simulations of two case studies, namely, a labscale microgrid and a commercial microgrid, are presented. We compare the performance of MPC with various horizon lengths to a rule-based control strategy to demonstrate a cost reduction of more than 2%.

Anjar Wanto - One of the best experts on this subject based on the ideXlab platform.

  • analysis of artificial neural network accuracy using backpropagation algorithm in predicting Process Forecasting
    IJISTECH (International Journal Of Information System & Technology), 2017
    Co-Authors: Sandy Putra Siregar, Anjar Wanto
    Abstract:

    Artificial Neural Networks are a computational paradigm formed based on the neural structure of intelligent organisms to gain better knowledge. Artificial neural networks are often used for various computing purposes. One of them is for prediction (Forecasting) data. The type of artificial neural network that is often used for prediction is the artificial neural network backpropagation because the backpropagation algorithm is able to learn from previous data and recognize the data pattern. So from this pattern backpropagation able to analyze and predict what will happen in the future. In this study, the data to be predicted is Human Development Index data from 2011 to 2015. Data sourced from the Central Bureau of Statistics of North Sumatra. This research uses 5 architectural models: 3-8-1, 3-18-1, 3-28-1, 3-16-1 and 3-48-1. From the 5 models of this architecture, the best accuracy is obtained from the architectural model 3-48-1 with 100% accuracy rate, with the epoch of 5480 iterations and MSE 0.0006386600 with error level 0.001 to 0.05. Thus, backpropagation algorithm using 3-48-1 model is good enough when used for data prediction.

Yiliu Liu - One of the best experts on this subject based on the ideXlab platform.

Michael A Osborne - One of the best experts on this subject based on the ideXlab platform.

  • optimal operation of an energy management system using model predictive control and gaussian Process time series modeling
    IEEE Journal of Emerging and Selected Topics in Power Electronics, 2018
    Co-Authors: Jaehwa Lee, Pengfei Zhang, Leong Kit Gan, David A Howey, Michael A Osborne, Alessandra Tosi, Stephen A Duncan
    Abstract:

    This paper describes an optimal operation scheme for energy management systems using Gaussian Process Forecasting and model predictive control (MPC) in the context of grid-connected microgrids with local generation, loads, and storage. The main objective of the control is to minimize the cost of energy taken from the grid. The microgrid consists of a photovoltaic (PV) panel and a battery energy storage system, which are connected to a power grid and a local load via a dc bus. At each sampling time, the predictions for PV output power and load demand power are calculated, and an MPC algorithm is executed based on these predictions and a physical battery model to decide the set point of the battery. Simulations of two case studies, namely, a labscale microgrid and a commercial microgrid, are presented. We compare the performance of MPC with various horizon lengths to a rule-based control strategy to demonstrate a cost reduction of more than 2%.